1. Technical Field
This invention relates to the field of quality control processes, and more specifically the use of image analysis in controlling overall quality of a dynamic production line.
2. Description of Related Art
A number of methods exist for analyzing the quality and sorting of food products transported on a conveyor belt. Such methods typically focus on sorting objects for the purpose of rejecting the objects having imperfections or defects and rejecting any foreign materials including non-edible parts of the food product. For example, manual efforts by people positioned along production lines and sorting by visual inspection as products pass along a conveyor belt provides one method of sorting or inspecting foods for quality control. Manual sorting is, however, costly and unreliable because of the inconsistent nature of human judgment by various individuals.
Computer vision and image analysis is an alternative and increasingly popular approach for automated, cost-effective methods for maintaining high and consistent quality standards. Computer vision systems are increasingly used in the food industry (including, for example, the grading or sorting of meats, grains, fish, pizza, cheese, or bread) for quality assurance purposes. Much of the literature on imaging analysis involves methods for altering the visual image in some way to make the image more visually appealing or to extract information on the shapes or boundaries of various observable features. In this vein, traditional image processes serve as automated, machine-vision systems performing operations many times faster and far more precisely than human observers or operators. Thus, such systems offer automated grading that can standardize techniques and eliminate tedious and inconsistent human inspection of product quality.
Among quality attributes, the coloring of a food product is of significance because consumers often use it as a basis for product selection or rejection. Color is one of the most significant inspection criteria used in the food industry in that the surface colorings of a food product may indicate the presence of defects or flaws in the food product. Such defects affect consumer acceptance or willingness to consume a product as well as point-of-sale value.
Color cameras are often used with machine or automated vision systems for image analysis systems or machine inspection of the quality of food products. But image analysis sorting methods of the food industry generally remain focused on sorting objects with the purpose of rejecting each product having any sort of defect, blemish, or otherwise visually unappealing characteristic. For example, existing sorting methods using image analysis in the production of food products sort out defective food products based on the degree of darkness and the size of the observed defect on the food product. In other words, most of the existing methods treat any defects as equal without regard for the relative area or intensity of the flaw or the size of a food product itself. Such sorting techniques result in higher amounts of wasted food product than might have been acceptable to consumers without compromising the overall perceived quality of the food. Efforts have been made to allow for sorting products based on the relative size of the defect compared to the total product surface area. Yet, even with these methods, the defect/rejection threshold is static and not adjusted for acceptability or preference factors while the products undergo quality inspection. This fails to account for the acceptability threshold per single item of the product in conjunction with the acceptability threshold for per batch, bag, or container.
Thus, it remains desirable to have sorting methods that not only sort food products potentially having defects but also evaluate the defects on the food products such that the amount of food products unnecessarily rejected or wasted is reduced. Such methods should take advantage of image analysis to provide reliable, objective, and cost-effective methods of producing food products while providing for nearly instantaneous monitoring and feedback control of the food products, especially when transitioning from one phase of assembly or preparation to another. Finally, such methods should also allow for quality control of food products to be ultimately packaged for consumption.